The Digital Services Research Group and its predecessor groups and offshoots in Digital Equipment Corporation have been mobilizing leading-edge AI research to bear on real-life problems that face the corporation and its customers. The general strategy of the group is to explore emerging techniques relevant to service and support needs through developing rapid prototypes, deploying these prototypes, and incorporating feedback from users. With over 32 major projects undertaken during the past decade, we have worked on broad spectrum of problems and explored a variety of advanced AI techniques. This article describes the current AI activities in five areas: (1) enterprise advisory systems, (2) natural language processing and textual information retrieval, (3) largescale knowledge base management and access, (4) software configuration management, and (5) intrusion detection. We also list some future research directions.

Allen Newell was one of the founders and truly great scientists of AI. His contributions included foundational concepts and ground-breaking systems. His career was defined by the pursuit of a single, fundamental issue: the nature of the human mind. This article traces his pursuit from his early work on search and list processing in systems such as the LOGIC THEORIST and the GENERAL PROBLEM SOLVER; through his work on problem spaces, human problem solving, and production systems; through his final work on unified theories of cognition and SOAR.

The Association for the Advancement of Artificial Intelligence held its 1992 Spring Symposium Series on March 25-27 at Stanford University, Stanford, California. This article contains a summary of the symposia that were conducted: Artificial Intelligence in Medicine, Cognitive Aspects of Knowledge Acquisition, Computational Considerations in Supporting Incremental Modification and Reuse, Knowledge Assimilation, Practical Approaches to Scheduling and Planning, Producing Cooperative Explanations, Propositional Knowledge Representation, Selective Perception, and Reasoning with Diagrammatic Representations.

An effective to develop an abstract UI model which solution to this problem must have three encompasses the essence of those systems.

My Ph.D. dissertation (Goel 1989) presents a computational model of experience-based design. It first reviews the core issues in experience-based design, for example, (1) the content of a design experience (or case), (2) the internal organization of design cases, (3) the language for indexing the cases, (4) the mechanism for retrieving a case relevant to a given design task, (5) the mechanism for adapting a retrieved design to satisfy the constraints of the design task, (6) the mechanism for evaluating a design against the specification of the design task, (7) the mechanism for redesigning a failed design, (8) the mechanism for acquiring new design knowledge, (9) the mechanism for chunking information about a design into a new case, and (10) the mechanism for storing a new case in memory for potential reuse in the future. It then proposes that decisions about these issues might lie in the designer's comprehension of the designs of artifacts he/she has encountered in the past, that is, in his/her mental models of how the designs achieve the functions and satisfy the constraints of the artifacts.

The workshop "Advances in Interfacing Production Systems with the Real World" was designed to bring together researchers from around the world to focus on the problem of integrating production systems into industrial environments. It was held on 25 August 1991 in Sydney, Australia, in conjunction with the Twelfth International Joint Conference on Artificial Intelligence (IJCAI-91). Nine papers were accepted for the proceedings, and six of them were discussed at the workshop.

The Association for the Advancement of Artificial Intelligence held its 1991 Fall Symposium Series on November 15-17 at the Asilomar Conference Center, Pacific Grove, California. This article contains summaries of the four symposia: Discourse Structure in Natural Language Understanding and Generation, Knowledge and Action at Social and Organizational Levels, Principles of Hybrid Reasoning, Sensory Aspects of Robotic Intelligence.

Integrated Language and Vision Systems, Scholarship Travel Program If you are interested in assisting AAAI at the national conference, New Mexico State University, Continued please contact AAAI at volunteer Dec. 1991 AAAI announces the continuation of @aaai.org. All inquiries should 1991 IFIP/KR Workshop its scholarship travel program for students include your name, address, telephone, Eleventh International Workshop on who want to attend the National advisor's name, and email Distributed Artificial Intelligence, Conference on Artificial Intelligence address. All requests to volunteer at Glen Arbor, Michigan, February 1992 in San Jose, California, 12-17 July AAAI-92 must be received by the 1992. First International Conference on and (2) are members of April 3 AAAI-92 Scholarship AI Planning Systems, University of AAAI. In addition, repeat scholarship Application Deadline Maryland, June 1992 applicants must have fulfilled the April 29 Al Magazine Summer Issue The Third International Conference volunteer and reporting requirements Calendar Deadline on Principles of Knowledge Representation for previous awards.

The Fredkin Challenge in AI Research and Applications at Chandrasekaran, B. see nlso Hendler, Match, 2(2): Summer 1981, 30-33.

My Ph.D. thesis (Ward 1992, 1991)1 addressed the task of generating natural language utterances. It was motivated by two difficulties in scaling up existing generators. Current generators only accept input that are relatively poor in information, such as feature structures or lists of propositions; they are unable to deal with input rich in information, as one might expect from, for example, an expert system with a complete model of its domain or a natural language understander with good inference ability. Current generators also have a very restricted knowledge of language -- indeed, they succeed largely because they have few syntactic or lexical options available (McDonald 1987) -- and they are unable to cope with more knowledge because they deal with interactions among the various possible choices only as special cases. To address these and other issues, I built a system called FIG (flexible incremental generator). FIG is based on a single associative network that encodes lexical knowledge, syntactic knowledge, and world knowledge. Computation is done by spreading activation across the network, supplemented with a small amount of symbolic processing. Thus, FIG is a spreading activation or structured connectionist system (Feldman et al. 1988).